OtherTube: Facilitating Content Discovery and Reflection by Exchanging YouTube Recommendations with Strangers
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Abstract (arXiv)
To promote engagement, recommendation algorithms on platforms like YouTube increasingly personalize users' feeds, limiting users' exposure to diverse content and depriving them of opportunities to reflect on their interests compared to others'. In this work, we investigate how exchanging recommendations with strangers can help users discover new content and reflect. We tested this idea by developing OtherTube -- a browser extension for YouTube that displays strangers' personalized YouTube recommendations. OtherTube allows users to (i) create an anonymized profile for social comparison, (ii) share their recommended videos with others, and (iii) browse strangers' YouTube recommendations. We conducted a 10-day-long user study (n=41) followed by a post-study interview (n=11). Our results reveal that users discovered and developed new interests from seeing OtherTube recommendations. We identified user and content characteristics that affect interaction and engagement with exchanged recommendations; for example, younger users interacted more with OtherTube, while the perceived irrelevance of some content discouraged users from watching certain videos. Users reflected on their interests as well as others', recognizing similarities and differences. Our work shows promise for designs leveraging the exchange of personalized recommendations with strangers.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Jan 2022.
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Citing papers per year: 2022: 4; 2023: 2; 2024: 2; 2025: 0; 2026: 1.
9 papers.
- 2024Authors' Values and Attitudes Towards AI-bridged Scalable Personalization of Creative Language Artscited by 13
- 2023The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Biascited by 12
- 2024Steering AI-Driven Personalization of Scientific Text for General Audiencescited by 7
- 2022Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectorscited by 0
- 2022Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networkscited by 0
- 2022Explanation-Guided Fairness Testing through Genetic Algorithmcited by 0
- 2022Using Graph Neural Networks for Program Terminationcited by 0
- 2023Tribe or Not? Critical Inspection of Group Differences Using TribalGramcited by 0
- 2026DRanker: A Transformer-Based Multi-Task Ranking Model for the Disney+ Homepagecited by 0
- 2026DRanker: A Transformer-Based Multi-Task Ranking Model for the Disney+ Homepage11 Oct 2026
- 2024Steering AI-Driven Personalization of Scientific Text for General Audiences15 Nov 2024
- 2024Authors' Values and Attitudes Towards AI-bridged Scalable Personalization of Creative Language Arts1 Mar 2024
- 2023The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias26 Dec 2023
- 2023Tribe or Not? Critical Inspection of Group Differences Using TribalGram16 Mar 2023
- 2022Using Graph Neural Networks for Program Termination28 Jul 2022
- 2022Explanation-Guided Fairness Testing through Genetic Algorithm16 May 2022
- 2022Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks8 Mar 2022
- 2022Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors6 Feb 2022